Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 48 | Pitched Project (Prof Manuel Hernandez) Insole for Gait Monitoring and Furthering Research of Fall Risk in Older Adults |
Jess Sun Lily Hyatt Nasym Kushner |
Kaiwen Cao | design_document1.pdf final_paper1.pdf grading_sheet1.pdf proposal1.pdf |
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| # Insole for Gait Monitoring and Furthering Research of Fall Risk in Older Adults Team Members: - Jessica Sun (jzsun2) - Nasym Kushner (nasymjk2) - Lily Hyatt (lhhyatt2) # Problem A major cause of injury, especially for the elderly population, is from falls. 8 million adults over the age of 65 are injured each year, and an estimated 3 million require emergency care for injuries. In the US alone, on average 32,000 deaths a year are due to falls, and worldwide, falls are the second most common cause of unintentional death. Currently, early smart home fall detection technology for high risk adults is lacking and fails to incorporate relevant data from monitoring changes in fall risk and frailty. As a response to the gap in the market, Dr. Manuel Hernandez’s lab created a TENG sensor designed for the insole. Our goal is to integrate the sensor into our device to monitor gait for data collection, and improvement and characterization of sensor. The device should be portable, allowing the user to walk as they would normally. It should be able to accurately convert the signals from the sensor into a digital format and transmit via Bluetooth. The challenges we face moving forward are: Measuring/dealing with high voltage (up to 40V) and low current (on the order of micro amps). Addressing the portability/wearability of the current sensor as well as its implementation into our design. And implementing and testing its self powering nature. # Solution As gait is one of the most important indicators of health, we also plan to improve development for a pressure sensing insole. This insole will have a custom triboelectric pressure sensor to analyze timing of the patient’s steps. An added feature of the triboelectric nanogenerator is its self powering ability. The main feature we plan on improving is usability. This will be accomplished through bluetooth integration with an easy to use mobile application which will store and display the collected data. This will make it easier to monitor patient status and enable further research on the effects of fall risk and fragility through data collection, advancing understanding of behavioral mechanisms related to balance and gait dysfunctions in older adults. The triboelectric sensor we will be working with is described as high voltage, low current. It detects load by passing current when changes in load are made. We aim to test the current custom triboelectric sensor to benchmark “high”, “medium”, and “low” loads based on factors such as weight, age, and gender and set thresholds to mark this as interpretable data for measuring step timing. We also need to create hardware that is comfortably wearable and compatible with the sensor, and synchronize the sensors from the left and right feet. As stated, the most important factor we plan to address is ease of usability. We understand that even though technology can unlock great opportunities for patient care, products that are difficult to use or incompatible diminishes these effects. As such, we strive to make our interface as user-friendly and intuitive as possible. Through the creation of a robust app, seamless data collection, and durable hardware, we hope to create a system patients and providers will enjoy using. # Solution Components ## Measurement Subsystem This subsystem measures step timing and load and makes the signal suitable for microcontroller. Pressure sensing insole (this component will be provided by Dr. Manuel Hernandez) Resistors (step high measurement voltage) diode (protect microcontroller against voltage spikes) capacitor (filter noise) ADS8689 (ADC) ## Data Processing Subsystem This subsystem process the measurements and exports them via Bluetooth) ESP-32 Bluetooth Module ## Power Subsystem This subsystem powers the data processing subsystem 3.3V Battery Power switch LED (indicate On/Off and status) ## Housing Subsystem Hold power and data processing subsystem Compact 3D printed case with spot for switch, LED, and openable battery compartment Velcro strap (for nearby attachment) ## Shoe Subsystem The sensor will be placed inside the sole of a sandal located on the heel. Orthopedic podiatric friendly sole with cutting to fit sensor Thin padding over the sensor for comfortability and protection of the sensor while not detracting from the load sensing capabilities ## Mobile Application Subsystem The app will receive Bluetooth data from the insole and display relevant information. Functions: Receive data from ESP-32 over bluetooth Display status of device Visualize and export data # Criterion For Success - Calibration of each sensor. Custom made sensors will have slight variations, so in order to capture the most standardized data sets between the two sensors worn on both feet, calibrations must be made. - Sensor accuracy. Data collected should have consistent readings under repeated same loading conditions. This should remain true under high step frequency (up to ~5Hz) - Voltage safety implementation. The voltage imputed into the microcontroller should always be within the rated voltage (3.3V or 5V depending on pin). - Ease of Use: The whole system (sole and user interface) should be easy and intuitive to use. The user should not have to worry about the internet settings on their device. The device should be easy to set up/install. - Durability: The product should be able to work properly and maintain accurate readings through rigorous usage over many cycles with variable loading weight and frequency. |
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